Monitoring and Maximizing Machine Utilization for Python Web Applications | Exam Answer

How to Monitor and Maximize Machine Utilization for Python Web Applications

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Question

You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production.

You want to monitor and maximize machine utilization.

You also want to reliably deploy new versions of the application.

Which set of steps should you take?

Answers

A. Perform the following: 1. Create a managed instance group with f1-micro type machines. 2. Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app. 3. Restart the instances to automatically deploy new production releases.

B. Perform the following: 1. Create a managed instance group with n1-standard-1 type machines. 2. Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app. 3. Rebuild the Compute Engine image, and update the instance template to deploy new production releases.

C. Perform the following: 1. Create a Google Kubernetes Engine (GKE) cluster with n1-standard-1 type machines. 2. Build a Docker image from the production branch with all of the dependencies, and tag it with the version number. 3. Create a Kubernetes Deployment with the imagePullPolicy set to `IfNotPresent` in the staging namespace, and then promote it to the production namespace after testing.

D. Perform the following: 1. Create a GKE cluster with n1-standard-4 type machines. 2. Build a Docker image from the master branch with all of the dependencies, and tag it with `latest`. 3. Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to `Always`. Restart the pods to automatically deploy new production releases.

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Explanations

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A. B. C. D.

The best option for this scenario is C. Perform the following: 1. Create a Google Kubernetes Engine (GKE) cluster with n1-standard-1 type machines. 2. Build a Docker image from the production branch with all of the dependencies, and tag it with the version number. 3. Create a Kubernetes Deployment with the imagePullPolicy set to IfNotPresent in the staging namespace, and then promote it to the production namespace after testing.

Here is a detailed explanation of each step:

Create a Google Kubernetes Engine (GKE) cluster with n1-standard-1 type machines: Kubernetes is an open-source platform designed to automate deploying, scaling, and operating application containers. GKE is a managed Kubernetes service provided by Google Cloud that eliminates the need to install and operate your Kubernetes cluster on your own. n1-standard-1 is a machine type that provides 1 virtual CPU and 3.75 GB of memory, which is more than enough for the requirements of the Python web application.
Build a Docker image from the production branch with all of the dependencies, and tag it with the version number: Docker is a platform that allows developers to build, ship, and run applications in containers. A container is a lightweight, standalone executable package of software that includes everything needed to run an application, including the code, libraries, dependencies, and settings. By building a Docker image, you can package your Python web application with all of its dependencies into a portable, self-contained unit that can be run anywhere Docker is installed. Tagging the Docker image with the version number allows you to track different versions of your application and roll back to a previous version if necessary.
Create a Kubernetes Deployment with the imagePullPolicy set to IfNotPresent in the staging namespace, and then promote it to the production namespace after testing: A Kubernetes Deployment is a Kubernetes resource that manages a set of replicated Pods. A Pod is the smallest deployable unit in Kubernetes and represents a single instance of a running process in a container. By creating a Deployment, you can specify the number of replicas of your application you want to run and how to deploy them. The imagePullPolicy set to IfNotPresent instructs Kubernetes to pull the Docker image only if it is not already present on the node. This reduces the time it takes to deploy the application and minimizes the amount of network traffic required. Creating the Deployment in the staging namespace allows you to test your application before deploying it to production. Once you are satisfied with the test results, you can promote the Deployment to the production namespace.

In summary, creating a GKE cluster with n1-standard-1 type machines, building a Docker image from the production branch with all of the dependencies, and creating a Kubernetes Deployment with the imagePullPolicy set to IfNotPresent in the staging namespace, and then promoting it to the production namespace after testing provides a reliable and scalable way to deploy and manage your Python web application in the cloud while maximizing machine utilization.

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